You will get Optimized SQL query for your ETL Project
Project details
With the real world experience in debugging and optimizing SQL queries, I'll optimize your SQL query. Understanding the purpose of the query and the necessary tables which are being used in the Query is the main crux of SQL query optimization.
My record SQL query optimization in which I optimized a query which included
• 20 tables with a table which has almost 1.2 billion records.
• 40 columns in the selection statement and 10 columns used in the aggregation
• Reduced the query execution time from 3 hours to 38 mins.
My record SQL query optimization in which I optimized a query which included
• 20 tables with a table which has almost 1.2 billion records.
• 40 columns in the selection statement and 10 columns used in the aggregation
• Reduced the query execution time from 3 hours to 38 mins.
Database Type
MySQL, MS SQL, Oracle, SQLite, PostgreSQL, MongoDBWhat's included
| Service Tiers |
Starter
$50
|
Standard
$90
|
Advanced
$100
|
|---|---|---|---|
| Delivery Time | 1 day | 2 days | 4 days |
Number of Revisions | Unlimited | Unlimited | Unlimited |
Number of Queries | 2 | 5 | 10 |
Query Debugging | - | - | |
Query Optimization | |||
Query Scheduling | - | - | - |
Query Analysis | - | ||
Source Code | - | - |
About Abdul
AWS ETL Specialist | Glue & Redshift Optimization | Fixing Broken Pipe
Bahawalpur, Pakistan - 9:51 am local time
I specialize in fixing and optimizing data pipelines built with Python, SQL, and AWS services. Most data systems don’t fail because they’re complex. They fail because of inefficient queries, incorrect incremental logic, poor partitioning, or missing error handling.
I focus on diagnosing the root cause and implementing clean, scalable solutions.
What I Help With
• Fixing AWS Glue job errors (memory issues, DiskFull, timeout failures)
• Optimizing slow SQL queries (Postgres, MySQL, Redshift)
• Building incremental ETL pipelines with proper CDC logic
• Removing duplicate loads and fixing broken joins
• API → S3 → RDS / Redshift data pipelines
• S3 partitioning strategy and performance tuning
• Improving ETL logging and monitoring
• Debugging Lambda-based data workflows
• Data warehouse optimization
• Preparing clean datasets for Power BI & Looker Studio
Technical Stack
- Python (Pandas, PySpark)
- SQL (Postgres, Redshift, MySQL)
- AWS Glue (Spark & Python Shell)
- AWS S3
- AWS Lambda
- AWS RDS
- Amazon Redshift
- GCS & BigQuery
- Power BI
- Looker Studio
My Approach
- Understand the failure or performance issue
- Analyze execution plans and pipeline flow
- Identify inefficiencies in queries or data movement
- Implement optimized logic (incremental loads, partitioning, indexing, batching)
- Ensure the solution is scalable and maintainable
I don’t just patch errors — I improve the structure so the issue doesn’t return.
Ideal Clients
• Startups scaling their data systems
• SaaS companies facing pipeline instability
• Teams migrating to AWS
• Businesses struggling with slow reporting
If your pipeline is breaking, slow, or poorly structured — let’s fix it properly.
Steps for completing your project
After purchasing the project, send requirements so Abdul can start the project.
Delivery time starts when Abdul receives requirements from you.
Abdul works on your project following the steps below.
Revisions may occur after the delivery date.
Understanding the Query
I'll first understand the query completely. Will test on dummy data. I can test on your data also if we are on development stage. I'll also understand the columns and there purpose in the query. check if the used columns are properly indexed or not.
Finding loop holes where Query is taking time
After understanding the query, I'll find the loop holes where query is getting maximum time. This mostly includes big table used in joins with the column which are not indexed properly.